diff --git a/example/auto_compression/detection/configs/picodet_reader.yml b/example/auto_compression/detection/configs/picodet_reader.yml index 389673367..cd7ba8029 100644 --- a/example/auto_compression/detection/configs/picodet_reader.yml +++ b/example/auto_compression/detection/configs/picodet_reader.yml @@ -6,13 +6,13 @@ TrainDataset: !COCODataSet image_dir: train2017 anno_path: annotations/instances_train2017.json - dataset_dir: dataset/coco/ + dataset_dir: /work/GETR-Lite-paddle-new/inference/datasets/coco/ EvalDataset: !COCODataSet image_dir: val2017 anno_path: annotations/instances_val2017.json - dataset_dir: dataset/coco/ + dataset_dir: /work/GETR-Lite-paddle-new/inference/datasets/coco/ eval_height: &eval_height 416 eval_width: &eval_width 416 diff --git a/example/auto_compression/detection/configs/ppyoloe_l_qat_dis.yaml b/example/auto_compression/detection/configs/ppyoloe_l_qat_dis.yaml index 7102142ed..248a5b8e2 100644 --- a/example/auto_compression/detection/configs/ppyoloe_l_qat_dis.yaml +++ b/example/auto_compression/detection/configs/ppyoloe_l_qat_dis.yaml @@ -2,7 +2,7 @@ Global: reader_config: configs/yolo_reader.yml arch: PPYOLOE - include_nms: True + include_nms: False Evaluation: True model_dir: ./ppyoloe_crn_l_300e_coco model_filename: model.pdmodel @@ -30,5 +30,4 @@ TrainConfig: optimizer_builder: optimizer: type: SGD - weight_decay: 4.0e-05 - + weight_decay: 4.0e-05 \ No newline at end of file diff --git a/example/auto_compression/detection/configs/ppyoloe_s_qat_dis.yaml b/example/auto_compression/detection/configs/ppyoloe_s_qat_dis.yaml index 3f6ade72b..60edb8db8 100644 --- a/example/auto_compression/detection/configs/ppyoloe_s_qat_dis.yaml +++ b/example/auto_compression/detection/configs/ppyoloe_s_qat_dis.yaml @@ -8,27 +8,39 @@ Global: model_filename: model.pdmodel params_filename: model.pdiparams -Distillation: - alpha: 1.0 - loss: soft_label +# Distillation: +# alpha: 1.0 +# loss: soft_label -QuantAware: - onnx_format: true - use_pact: true - activation_quantize_type: 'moving_average_abs_max' - quantize_op_types: - - conv2d - - depthwise_conv2d +# QuantAware: +# onnx_format: true +# use_pact: true +# activation_quantize_type: 'moving_average_abs_max' +# quantize_op_types: +# - conv2d +# - depthwise_conv2d -TrainConfig: - train_iter: 5000 - eval_iter: 1000 - learning_rate: - type: CosineAnnealingDecay - learning_rate: 0.00003 - T_max: 6000 - optimizer_builder: - optimizer: - type: SGD - weight_decay: 4.0e-05 +# TrainConfig: +# train_iter: 5000 +# eval_iter: 1000 +# learning_rate: +# type: CosineAnnealingDecay +# learning_rate: 0.00003 +# T_max: 6000 +# optimizer_builder: +# optimizer: +# type: SGD +# weight_decay: 4.0e-05 +QuantPost: + batch_size: 32 + batch_nums: None + algo: 'hist' + hist_percent: 0.999 + bias_correct: False + recon_level: None + regions: None + epochs: 20 + lr: 0.1 + simulate_activation_quant: False + skip_tensor_list: None diff --git a/example/auto_compression/detection/configs/yolo_reader.yml b/example/auto_compression/detection/configs/yolo_reader.yml index d10614530..6e013c1b9 100644 --- a/example/auto_compression/detection/configs/yolo_reader.yml +++ b/example/auto_compression/detection/configs/yolo_reader.yml @@ -6,13 +6,13 @@ TrainDataset: !COCODataSet image_dir: train2017 anno_path: annotations/instances_train2017.json - dataset_dir: dataset/coco/ + dataset_dir: /work/GETR-Lite-paddle-new/inference/datasets/coco/ EvalDataset: !COCODataSet image_dir: val2017 anno_path: annotations/instances_val2017.json - dataset_dir: dataset/coco/ + dataset_dir: /work/GETR-Lite-paddle-new/inference/datasets/coco/ worker_num: 0 diff --git a/example/auto_compression/detection/paddle_inference_eval.py b/example/auto_compression/detection/paddle_inference_eval.py index d2e12afd1..b0368bffb 100644 --- a/example/auto_compression/detection/paddle_inference_eval.py +++ b/example/auto_compression/detection/paddle_inference_eval.py @@ -18,6 +18,7 @@ import sys import cv2 import numpy as np +from tqdm import tqdm import paddle from paddle.inference import Config @@ -82,9 +83,15 @@ def argsparser(): parser.add_argument("--img_shape", type=int, default=640, help="input_size") parser.add_argument( '--include_nms', - type=bool, - default=True, + type=str, + default='True', help="Whether include nms or not.") + parser.add_argument( + "--trt_calib_mode", + type=bool, + default=False, + help="If the model is produced by TRT offline quantitative " + "calibration, trt_calib_mode need to set True.") return parser @@ -208,8 +215,9 @@ def load_predictor( use_mkldnn=False, batch_size=1, device="CPU", - min_subgraph_size=3, + min_subgraph_size=4, use_dynamic_shape=False, + trt_calib_mode=False, trt_min_shape=1, trt_max_shape=1280, trt_opt_shape=640, @@ -238,9 +246,11 @@ def load_predictor( config = Config( os.path.join(model_dir, "model.pdmodel"), os.path.join(model_dir, "model.pdiparams")) + + config.enable_memory_optim() if device == "GPU": # initial GPU memory(M), device ID - config.enable_use_gpu(200, 0) + config.enable_use_gpu(1000, 0) # optimize graph and fuse op config.switch_ir_optim(True) else: @@ -260,12 +270,12 @@ def load_predictor( } if precision in precision_map.keys() and use_trt: config.enable_tensorrt_engine( - workspace_size=(1 << 25) * batch_size, + workspace_size=(1 << 30) * batch_size, max_batch_size=batch_size, min_subgraph_size=min_subgraph_size, precision_mode=precision_map[precision], use_static=True, - use_calib_mode=False, ) + use_calib_mode=False) if use_dynamic_shape: dynamic_shape_file = os.path.join(FLAGS.model_path, @@ -297,6 +307,7 @@ def predict_image(predictor, img, scale_factor = image_preprocess(image_file, image_shape) inputs = {} inputs["image"] = img + if FLAGS.include_nms: inputs['scale_factor'] = scale_factor input_names = predictor.get_input_names() @@ -356,7 +367,8 @@ def eval(predictor, val_loader, metric, rerun_flag=False): boxes_tensor = predictor.get_output_handle(output_names[0]) if FLAGS.include_nms: boxes_num = predictor.get_output_handle(output_names[1]) - for batch_id, data in enumerate(val_loader): + for batch_id, data in tqdm( + enumerate(val_loader), total=len(val_loader), desc='Evaluating'): data_all = {k: np.array(v) for k, v in data.items()} for i, _ in enumerate(input_names): input_tensor = predictor.get_input_handle(input_names[i]) @@ -382,7 +394,6 @@ def eval(predictor, val_loader, metric, rerun_flag=False): res = {'bbox': np_boxes, 'bbox_num': np_boxes_num} metric.update(data_all, res) if batch_id % 100 == 0: - print("Eval iter:", batch_id) sys.stdout.flush() metric.accumulate() metric.log() @@ -421,7 +432,6 @@ def main(): repeats=repeats) else: reader_cfg = load_config(FLAGS.reader_config) - dataset = reader_cfg["EvalDataset"] global val_loader val_loader = create("EvalReader")( @@ -432,6 +442,7 @@ def main(): anno_file = dataset.get_anno() metric = COCOMetric( anno_file=anno_file, clsid2catid=clsid2catid, IouType="bbox") + eval(predictor, val_loader, metric, rerun_flag=rerun_flag) if rerun_flag: @@ -444,6 +455,10 @@ def main(): paddle.enable_static() parser = argsparser() FLAGS = parser.parse_args() + if FLAGS.include_nms == 'True': + FLAGS.include_nms = True + else: + FLAGS.include_nms = False # DataLoader need run on cpu paddle.set_device("cpu") diff --git a/example/auto_compression/detection/post_process.py b/example/auto_compression/detection/post_process.py index eea2f0195..4ed79ce73 100644 --- a/example/auto_compression/detection/post_process.py +++ b/example/auto_compression/detection/post_process.py @@ -41,8 +41,7 @@ def hard_nms(box_scores, iou_threshold, top_k=-1, candidate_size=200): rest_boxes = boxes[indexes, :] iou = iou_of( rest_boxes, - np.expand_dims( - current_box, axis=0), ) + np.expand_dims(current_box, axis=0), ) indexes = indexes[iou <= iou_threshold] return box_scores[picked, :] @@ -122,7 +121,7 @@ def _non_max_suppression(self, prediction, scale_factor): picked_labels.extend([class_index] * box_probs.shape[0]) if len(picked_box_probs) == 0: - out_boxes_list.append(np.empty((0, 4))) + out_boxes_list.append(np.empty((0, 6))) else: picked_box_probs = np.concatenate(picked_box_probs) @@ -135,9 +134,8 @@ def _non_max_suppression(self, prediction, scale_factor): # clas score box out_box = np.concatenate( [ - np.expand_dims( - np.array(picked_labels), axis=-1), np.expand_dims( - picked_box_probs[:, 4], axis=-1), + np.expand_dims(np.array(picked_labels), axis=-1), + np.expand_dims(picked_box_probs[:, 4], axis=-1), picked_box_probs[:, :4] ], axis=1) @@ -152,6 +150,6 @@ def _non_max_suppression(self, prediction, scale_factor): return out_boxes_list, box_num_list def __call__(self, outs, scale_factor): - out_boxes_list, box_num_list = self._non_max_suppression(outs, - scale_factor) + out_boxes_list, box_num_list = self._non_max_suppression( + outs, scale_factor) return {'bbox': out_boxes_list, 'bbox_num': box_num_list} diff --git a/example/auto_compression/nlp/configs/pp-minilm/auto/afqmc.yaml b/example/auto_compression/nlp/configs/pp-minilm/auto/afqmc.yaml index 9c9f58826..8244c90c3 100644 --- a/example/auto_compression/nlp/configs/pp-minilm/auto/afqmc.yaml +++ b/example/auto_compression/nlp/configs/pp-minilm/auto/afqmc.yaml @@ -6,11 +6,20 @@ Global: dataset: clue batch_size: 16 max_seq_length: 128 -TransformerPrune: - pruned_ratio: 0.25 -HyperParameterOptimization: + + +# 蒸馏 Distillation: -QuantPost: + teacher_model_dir: ./afqmc + teacher_model_filename: inference.pdmodel + teacher_params_filename: inference.pdiparams + +# 剪枝参数 +# 剪枝参数包括剪枝算法和裁剪度 +Prune: + prune_algo: transformer_pruner + pruned_ratio: 0.25 + TrainConfig: epochs: 6 eval_iter: 1070 @@ -20,3 +29,12 @@ TrainConfig: type: AdamW weight_decay: 0.01 origin_metric: 0.7403 + + +# 离线量化 +QuantPost: + activation_bits: 8 + quantize_op_types: + - conv2d + - depthwise_conv2d + weight_bits: 8 diff --git a/example/auto_compression/nlp/configs/uie/uie_base.yaml b/example/auto_compression/nlp/configs/uie/uie_base.yaml index 484f62899..36873084f 100644 --- a/example/auto_compression/nlp/configs/uie/uie_base.yaml +++ b/example/auto_compression/nlp/configs/uie/uie_base.yaml @@ -2,21 +2,24 @@ Global: model_dir: ./UIE model_filename: inference.pdmodel params_filename: inference.pdiparams - batch_size: 1 - max_seq_length: 512 - train_data: ./data/train.txt - dev_data: ./data/dev.txt -TrainConfig: - epochs: 200 - eval_iter: 100 - learning_rate: 1.0e-5 - optimizer_builder: - optimizer: - type: AdamW - weight_decay: 0.01 + task_name: afqmc + dataset: clue + batch_size: 16 + max_seq_length: 128 -QuantAware: - onnx_format: True -Distillation: - alpha: 1.0 - loss: l2 + +HyperParameterOptimization: + batch_num: + - 4 + - 16 + bias_correct: + - true + hist_percent: + - 0.999 + - 0.99999 + max_quant_count: 20 + ptq_algo: + - KL + - hist + weight_quantize_type: + - channel_wise_abs_max \ No newline at end of file diff --git a/example/auto_compression/nlp/run.py b/example/auto_compression/nlp/run.py index 1f6fa5403..5bfac56db 100644 --- a/example/auto_compression/nlp/run.py +++ b/example/auto_compression/nlp/run.py @@ -17,6 +17,8 @@ from paddlenlp.metrics import Mcc, PearsonAndSpearman from paddleslim.common import load_config from paddleslim.auto_compression.compressor import AutoCompression +import sys +sys.setrecursionlimit(1500) # 设置一个更高的限制，例如 1500 def argsparser(): diff --git a/example/auto_compression/pytorch_yolo_series/paddle_inference_eval.py b/example/auto_compression/pytorch_yolo_series/paddle_inference_eval.py index a1df31b78..ea5cb975d 100644 --- a/example/auto_compression/pytorch_yolo_series/paddle_inference_eval.py +++ b/example/auto_compression/pytorch_yolo_series/paddle_inference_eval.py @@ -79,7 +79,8 @@ def argsparser(): "--device", type=str, default="GPU", - help="Choose the device you want to run, it can be: CPU/GPU/XPU, default is GPU", + help= + "Choose the device you want to run, it can be: CPU/GPU/XPU, default is GPU", ) parser.add_argument( "--arch", type=str, default="YOLOv5", help="architectures name.") @@ -180,8 +181,9 @@ def draw_box(img, boxes, scores, cls_ids, conf=0.5, class_names=None): txt_size = cv2.getTextSize(text, font, 0.4, 1)[0] cv2.rectangle(img, (x0, y0), (x1, y1), color, 2) - cv2.rectangle(img, (x0, y0 + 1), ( - x0 + txt_size[0] + 1, y0 + int(1.5 * txt_size[1])), color, -1) + cv2.rectangle(img, (x0, y0 + 1), (x0 + txt_size[0] + 1, + y0 + int(1.5 * txt_size[1])), color, + -1) cv2.putText( img, text, (x0, y0 + txt_size[1]), @@ -288,8 +290,8 @@ def load_predictor( dynamic_shape_file = os.path.join(FLAGS.model_path, "dynamic_shape.txt") if os.path.exists(dynamic_shape_file): - config.enable_tuned_tensorrt_dynamic_shape(dynamic_shape_file, - True) + config.enable_tuned_tensorrt_dynamic_shape( + dynamic_shape_file, True) print("trt set dynamic shape done!") else: config.collect_shape_range_info(dynamic_shape_file) @@ -315,7 +317,8 @@ def eval(predictor, val_loader, anno_file, rerun_flag=False): input_names = predictor.get_input_names() output_names = predictor.get_output_names() boxes_tensor = predictor.get_output_handle(output_names[0]) - for batch_id, data in enumerate(val_loader): + for batch_id, data in tqdm( + enumerate(val_loader), total=len(val_loader), desc='Evaluating'): data_all = {k: np.array(v) for k, v in data.items()} inputs = {} if FLAGS.arch == "YOLOv6": @@ -345,7 +348,7 @@ def eval(predictor, val_loader, anno_file, rerun_flag=False): cpu_mems += cpu_mem gpu_mems += gpu_mem if batch_id % 100 == 0: - print("Eval iter:", batch_id) + # print("Eval iter:", batch_id) sys.stdout.flush() print("[Benchmark]Avg cpu_mem:{} MB, avg gpu_mem: {} MB".format( cpu_mems / sample_nums, gpu_mems / sample_nums)) diff --git a/example/post_training_quantization/detection/configs/picodet_s_analysis.yaml b/example/post_training_quantization/detection/configs/picodet_s_analysis.yaml index d3d6944c2..16a134c87 100644 --- a/example/post_training_quantization/detection/configs/picodet_s_analysis.yaml +++ b/example/post_training_quantization/detection/configs/picodet_s_analysis.yaml @@ -1,12 +1,12 @@ input_list: ['image', 'scale_factor'] -model_dir: ./picodet_s_416_coco_lcnet/ +model_dir: ./picodet_s_416_coco_lcnet model_filename: model.pdmodel params_filename: model.pdiparams save_dir: ./analysis_results metric: COCO num_classes: 80 plot_hist: True -get_target_quant_model: False +get_target_quant_model: None target_metric: None PTQ: @@ -22,15 +22,15 @@ EvalDataset: !COCODataSet image_dir: val2017 anno_path: annotations/instances_val2017.json - dataset_dir: /dataset/coco/ + dataset_dir: /work/GETR-Lite-paddle-new/inference/datasets/coco/ # Small Dataset to accelerate analysis # If not exist, delete the dict of FastEvalDataset -FastEvalDataset: - !COCODataSet - image_dir: val2017 - anno_path: annotations/small_instances_val2017.json - dataset_dir: /dataset/coco/ +# FastEvalDataset: +# !COCODataSet +# image_dir: val2017 +# anno_path: annotations/small_instances_val2017.json +# dataset_dir: /dataset/coco/ eval_height: &eval_height 416 diff --git a/example/post_training_quantization/detection/configs/picodet_s_analyzed_ptq.yaml b/example/post_training_quantization/detection/configs/picodet_s_analyzed_ptq.yaml index 54aa3cb9c..6c3ea4721 100644 --- a/example/post_training_quantization/detection/configs/picodet_s_analyzed_ptq.yaml +++ b/example/post_training_quantization/detection/configs/picodet_s_analyzed_ptq.yaml @@ -12,13 +12,13 @@ TrainDataset: !COCODataSet image_dir: train2017 anno_path: annotations/instances_train2017.json - dataset_dir: /paddle/dataset/coco/ + dataset_dir: /work/GETR-Lite-paddle-new/inference/datasets/coco/ EvalDataset: !COCODataSet image_dir: val2017 anno_path: annotations/instances_val2017.json - dataset_dir: /paddle/dataset/coco/ + dataset_dir: /work/GETR-Lite-paddle-new/inference/datasets/coco/ eval_height: &eval_height 416 eval_width: &eval_width 416 diff --git a/example/post_training_quantization/detection/configs/picodet_s_ptq.yaml b/example/post_training_quantization/detection/configs/picodet_s_ptq.yaml index 005c0d46c..a1c5cb70a 100644 --- a/example/post_training_quantization/detection/configs/picodet_s_ptq.yaml +++ b/example/post_training_quantization/detection/configs/picodet_s_ptq.yaml @@ -1,5 +1,5 @@ input_list: ['image', 'scale_factor'] -model_dir: ./picodet_s_416_coco_lcnet/ +model_dir: ./picodet_s_analyzed_ptq_out model_filename: model.pdmodel params_filename: model.pdiparams skip_tensor_list: None @@ -12,13 +12,13 @@ TrainDataset: !COCODataSet image_dir: train2017 anno_path: annotations/instances_train2017.json - dataset_dir: /dataset/coco/ + dataset_dir: /work/GETR-Lite-paddle-new/inference/datasets/coco/ EvalDataset: !COCODataSet image_dir: val2017 anno_path: annotations/instances_val2017.json - dataset_dir: /dataset/coco/ + dataset_dir: /work/GETR-Lite-paddle-new/inference/datasets/coco/ eval_height: &eval_height 416 eval_width: &eval_width 416 @@ -34,5 +34,5 @@ EvalReader: - Resize: {interp: 2, target_size: *eval_size, keep_ratio: False} - NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]} - Permute: {} - batch_size: 32 + batch_size: 16 diff --git a/example/post_training_quantization/detection/configs/ppyoloe_s_ptq.yaml b/example/post_training_quantization/detection/configs/ppyoloe_s_ptq.yaml index 3c8752652..5fcf7212d 100644 --- a/example/post_training_quantization/detection/configs/ppyoloe_s_ptq.yaml +++ b/example/post_training_quantization/detection/configs/ppyoloe_s_ptq.yaml @@ -1,4 +1,4 @@ -input_list: ['image'] +input_list: ['image', 'scale_factor'] arch: PPYOLOE # When export exclude_nms=True, need set arch: PPYOLOE model_dir: ./ppyoloe_crn_s_300e_coco model_filename: model.pdmodel @@ -12,13 +12,13 @@ TrainDataset: !COCODataSet image_dir: train2017 anno_path: annotations/instances_train2017.json - dataset_dir: /dataset/coco/ + dataset_dir: /work/GETR-Lite-paddle-new/inference/datasets/coco/ EvalDataset: !COCODataSet image_dir: val2017 anno_path: annotations/instances_val2017.json - dataset_dir: /dataset/coco/ + dataset_dir: /work/GETR-Lite-paddle-new/inference/datasets/coco/ worker_num: 0 @@ -29,4 +29,4 @@ EvalReader: - Resize: {target_size: [640, 640], keep_ratio: False, interp: 2} - NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True} - Permute: {} - batch_size: 32 \ No newline at end of file + batch_size: 16 \ No newline at end of file diff --git a/example/post_training_quantization/detection/eval.py b/example/post_training_quantization/detection/eval.py index f8e1342d5..47fe16225 100644 --- a/example/post_training_quantization/detection/eval.py +++ b/example/post_training_quantization/detection/eval.py @@ -97,10 +97,11 @@ def eval(): if k in config['input_list'].keys(): data_input[config['input_list'][k]] = np.array(v) - outs = exe.run(val_program, - feed=data_input, - fetch_list=fetch_targets, - return_numpy=False) + outs = exe.run( + val_program, + feed=data_input, + fetch_list=fetch_targets, + return_numpy=False) res = {} if 'arch' in config and config['arch'] == 'keypoint': res = keypoint_post_process(data, data_input, exe, val_program, @@ -112,6 +113,7 @@ def eval(): else: for out in outs: v = np.array(out) + # print("v",v) if len(v.shape) > 1: res['bbox'] = v else: @@ -130,9 +132,8 @@ def main(): dataset = config['EvalDataset'] global val_loader - val_loader = create('EvalReader')(config['EvalDataset'], - config['worker_num'], - return_list=True) + val_loader = create('EvalReader')( + config['EvalDataset'], config['worker_num'], return_list=True) metric = None if config['metric'] == 'COCO': clsid2catid = {v: k for k, v in dataset.catid2clsid.items()} diff --git a/example/post_training_quantization/pytorch_yolo_series/README.md b/example/post_training_quantization/pytorch_yolo_series/README.md index 4bb4d304f..63a7d96c1 100755 --- a/example/post_training_quantization/pytorch_yolo_series/README.md +++ b/example/post_training_quantization/pytorch_yolo_series/README.md @@ -122,7 +122,7 @@ python eval.py --config_path=./configs/yolov5s_ptq.yaml #### 3.6 提高离线量化精度 ###### 3.6.1 量化分析工具 -本节介绍如何使用量化分析工具提升离线量化精度。离线量化功能仅需使用少量数据，且使用简单、能快速得到量化模型，但往往会造成较大的精度损失。PaddleSlim提供量化分析工具，会使用接口```paddleslim.quant.AnalysisPTQ```，可视化展示出不适合量化的层，通过跳过这些层，提高离线量化模型精度。```paddleslim.quant.AnalysisPTQ```详解见[AnalysisPTQ.md](../../../docs/zh_cn/tutorials/quant/AnalysisPTQ.md)。 +本节介绍如何使用量化分析工具提升离线量化精度。离线量化功能仅需使用少量数据，且使用简单、能快速得到量化模型，但往往会造成较大的精度损失。PaddleSlim提供量化分析工具，会使用接口```paddleslim.quant.AnalysisPTQ```，可视化展示出不适合量化的层，通过跳过这些层，提高离线量化模型精度。```paddleslim.quant.AnalysisPTQ```详解见[AnalysisPTQ.md](https://github.com/PaddlePaddle/PaddleSlim/blob/develop/docs/zh_cn/tutorials/quant/post_training_quantization.md)。 由于YOLOv6离线量化效果较差，以YOLOv6为例，量化分析工具具体使用方法如下： @@ -207,7 +207,70 @@ python fine_tune.py --config_path=./configs/yolov6s_fine_tune.yaml --simulate_ac ## 4.预测部署 预测部署可参考[YOLO系列模型自动压缩示例](https://github.com/PaddlePaddle/PaddleSlim/tree/develop/example/auto_compression/pytorch_yolo_series) - - +量化模型在GPU上可以使用TensorRT进行加速，在CPU上可以使用MKLDNN进行加速。 +| 参数名 | 含义 | +| model_path | inference模型文件所在路径，该目录下需要有文件model.pdmodel和params.pdiparams两个文件 | +| dataset_dir | 指定COCO数据集的目录，这是存储数据集的根目录 | +| image_file | 如果只测试单张图片效果，直接根据image_file指定图片路径 | +| val_image_dir | COCO数据集中验证图像的目录名，默认为val2017 | +| val_anno_path | 指定COCO数据集的注释(annotation)文件路径，这是包含验证集标注信息的JSON文件，默认为annotations/instances_val2017.json | +| benchmark | 指定是否运行性能基准测试。如果设置为True，程序将会进行性能测试 | +| device | 使用GPU或者CPU预测，可选CPU/GPU/XPU，默认设置为GPU | +| use_trt | 是否使用TensorRT进行预测| +| use_mkldnn | 是否使用MKL-DNN加速库，注意use_mkldnn与use_gpu同时为True时,将忽略enable_mkldnn,而使用GPU预测| +| use_dynamic_shape | 是否使用动态形状(dynamic_shape)功能 | +| precision | fp32/fp16/int8| +| arch | 指定所使用的模型架构的名称，例如YOLOv5 | +| img_shape | 指定模型输入的图像尺寸 | +| batch_size | 指定模型输入的批处理大小 | +| use_mkldnn | 指定是否使用MKLDNN加速(主要针对CPU)| +| cpu_threads | 指定在CPU上使用的线程数 | + +首先，我们拥有的yolov6.onnx，我们需要把ONNX模型转成paddle模型，具体参考使用[X2Paddle迁移推理模型](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/model_convert/convert_with_x2paddle_cn.html#x2paddle) +- 安装X2Paddle +方式一:pip 安装 +```shell +pip install X2Paddle==1.3.9 +``` +方式二:源码安装 +```shell +git clone https://github.com/PaddlePaddle/X2Paddle.git +cd X2Paddle +python setup.py install +``` +使用命令将YOLOv6.onnx模型转换成paddle模型 +```shell +x2paddle --framework=onnx --model=yolov6s.onnx --save_dir=yolov6_model +``` +- TensorRT Python部署 +使用[paddle_inference_eval.py](https://github.com/PaddlePaddle/PaddleSlim/blob/develop/example/auto_compression/pytorch_yolo_series/paddle_inference_eval.py)部署 +```shell +python paddle_inference_eval.py --model_path=yolov6_model/inference_model --dataset_dir=datasets/coco --use_trt=True --precision=fp32 --arch=YOLOv6 +``` +执行int8量化 +```shell +python paddle_inference_eval.py --model_path=yolov6s_ptq_out --dataset_dir==datasets/coco --use_trt=True --precision=int8 --arch=YOLOv6 +``` +- C++部署 +具体可参考[运行PP-YOLOE-l目标检测模型样例](https://github.com/PaddlePaddle/Paddle-Inference-Demo/tree/master/c%2B%2B/gpu/ppyoloe_crn_l) +将compile.sh中DEMO_NAME修改为yolov6_test，并且将ppyoloe_crn_l.cc修改为yolov6_test.cc,根据环境修改相关配置库 +运行bash compile.sh编译样例。 +- 运行样例 +-使用原生GPU运行样例(将ONNX模型转成的paddle模型复制到Paddle-Inference-demo/c++/gpu/ppyoloe_crn_l/目录下) +```shell +./build/yolov6_test --model_file yolov6s_infer/model.pdmodel --params_file yolov6s_infer/model.pdiparams +``` +- 使用TensorRT FP32运行样例 +```shell +./build/yolov6_test --model_file yolov6s_infer/model.pdmodel --params_file yolov6s_infer/model.pdiparams --run_mode=trt_fp32 +``` +- 使用TensorRT FP16运行样例 +```shell +./build/yolov6_test --model_file yolov6s_infer/model.pdmodel --params_file yolov6s_infer/model.pdiparams --run_mode=trt_fp16 +``` +- 使用TensorRT INT8运行样例 +```shell +./build/yolov6_test --model_file yolov6s_infer/model.pdmodel --params_file yolov6s_infer/model.pdiparams --run_mode=trt_int8 +``` ## 5.FAQ - 如果想对模型进行自动压缩，可进入[YOLO系列模型自动压缩示例](https://github.com/PaddlePaddle/PaddleSlim/tree/develop/example/auto_compression/pytorch_yolo_series)中进行实验。 diff --git a/setup.py b/setup.py index bc2842802..a2f84f961 100644 --- a/setup.py +++ b/setup.py @@ -22,17 +22,18 @@ from setuptools import find_packages from setuptools import setup -if 'develop' in subprocess.getoutput('git branch'): - slim_version = '0.0.0_dev' -else: - tag_list = subprocess.getoutput('git tag').split('\n') - if 'rc' in tag_list[-1]: - if tag_list[-1].split('rc')[0] in tag_list[-2]: - slim_version = tag_list[-2] - else: - slim_version = tag_list[-1] - else: - slim_version = tag_list[-1] +# if 'develop' in subprocess.getoutput('git branch'): +# slim_version = '0.0.0_dev' +# else: +# tag_list = subprocess.getoutput('git tag').split('\n') +# if 'rc' in tag_list[-1]: +# if tag_list[-1].split('rc')[0] in tag_list[-2]: +# slim_version = tag_list[-2] +# else: +# slim_version = tag_list[-1] +# else: +# slim_version = tag_list[-1] +slim_version = '2.6.0' with open('./requirements.txt') as f: setup_requires = f.read().splitlines()